Analysis date: 2023-08-08

Depends on

CRC_Xenografts_Batch2_DataProcessing Script

load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")

TODO

Setup

Load libraries and functions

Analysis

DEP

Tyrosine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set4, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway       pval
## 1:                                          2-LTR circle formation 0.51262136
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.45995893
## 3:                                       ABC transporter disorders 0.73818182
## 4:                          ABC-family proteins mediated transport 0.73818182
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.06990291
## 6:                       ADP signalling through P2Y purinoceptor 1 0.73350923
##         padj    log2err         ES        NES size               leadingEdge
## 1: 0.9022136 0.07627972  0.7670455  1.0078139    1                      2547
## 2: 0.8669296 0.08504275 -0.7642045 -1.0260971    1                      6385
## 3: 0.9295892 0.09054289 -0.3132184 -0.7678948    5 10213,5687,5692,5706,5683
## 4: 0.9295892 0.09054289 -0.3132184 -0.7678948    5 10213,5687,5692,5706,5683
## 5: 0.3917201 0.23779383  0.9659091  1.2690989    1                      5575
## 6: 0.9295892 0.07362127 -0.5023155 -0.8238637    2                      1432

## Note: Row-scaling applied for this heatmap

data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway       pval
## 1:                                          2-LTR circle formation 0.76938370
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.87975952
## 3:                                       ABC transporter disorders 0.61611374
## 4:                          ABC-family proteins mediated transport 0.61611374
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.01502038
## 6:                       ADP signalling through P2Y purinoceptor 1 0.57013575
##         padj    log2err         ES        NES size          leadingEdge
## 1: 0.9611015 0.05748774  0.6079545  0.8181126    1                 2547
## 2: 0.9631800 0.05163560 -0.5511364 -0.7391324    1                 6385
## 3: 0.9611015 0.05712585  0.4402128  0.9030274    5 5683,5706,5692,10213
## 4: 0.9611015 0.05712585  0.4402128  0.9030274    5 5683,5706,5692,10213
## 5: 0.8534483 0.38073040  0.9971591  1.3418576    1                 5575
## 6: 0.9611015 0.07871138 -0.5818796 -0.9175830    2                 1432
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway       pval
## 1:                                          2-LTR circle formation 0.29065041
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.69785575
## 3:                                       ABC transporter disorders 0.73214286
## 4:                          ABC-family proteins mediated transport 0.73214286
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.02873552
## 6:                       ADP signalling through P2Y purinoceptor 1 0.79639175
##         padj    log2err         ES        NES size               leadingEdge
## 1: 0.9773596 0.11191832  0.8664773  1.1525733    1                      2547
## 2: 0.9773596 0.06116926 -0.6761364 -0.8888176    1                      6385
## 3: 0.9773596 0.08998608 -0.3247126 -0.8176925    5 5687,10213,5692,5683,5706
## 4: 0.9773596 0.08998608 -0.3247126 -0.8176925    5 5687,10213,5692,5683,5706
## 5: 0.9016803 0.35248786  0.9857955  1.3112883    1                      5575
## 6: 0.9773596 0.06831109 -0.4729345 -0.7822898    2                 1432,6714

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set4, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway       pval
## 1:                                          2-LTR circle formation 0.88631985
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.44969199
## 3:                                       ABC transporter disorders 0.86685160
## 4:                          ABC-family proteins mediated transport 0.86685160
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.09856263
## 6:                       ADP signalling through P2Y purinoceptor 1 0.79545455
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.9713542 0.04949049 -0.5625000 -0.7522388    1        2547
## 2: 0.8256003 0.08628656  0.7755682  1.0387136    1        6385
## 3: 0.9713542 0.03539106 -0.3547866 -0.7085520    5        5687
## 4: 0.9713542 0.03539106 -0.3547866 -0.7085520    5        5687
## 5: 0.4183188 0.20429476  0.9431818  1.2631975    1        5575
## 6: 0.9713542 0.04660151 -0.5017764 -0.7777033    2        1432
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set4, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 1 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)
##                                                            pathway       pval
## 1:                                          2-LTR circle formation 0.24302789
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.08548708
## 3:                                       ABC transporter disorders 0.99617347
## 4:                          ABC-family proteins mediated transport 0.99617347
## 5:         ADORA2B mediated anti-inflammatory cytokines production 0.40954274
## 6:                       ADP signalling through P2Y purinoceptor 1 0.30200308
##         padj    log2err         ES        NES size          leadingEdge
## 1: 0.9380927 0.12267919  0.8778409  1.1833922    1                 2547
## 2: 0.8933684 0.21654284 -0.9545455 -1.2828719    1                 6385
## 3: 1.0000000 0.02421535  0.2075016  0.4366359    5 5706,10213,5692,5687
## 4: 1.0000000 0.02421535  0.2075016  0.4366359    5 5706,10213,5692,5687
## 5: 0.9509109 0.08971047 -0.7954545 -1.0690599    1                 5575
## 6: 0.9509109 0.09255289  0.7192957  1.1434051    2            1432,6714

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

Serine/Threonine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set4, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                     pathway      pval      padj
## 1:                                   2-LTR circle formation 0.8850103 0.9540714
## 2:                   ABC-family proteins mediated transport 0.3812261 0.7508441
## 3:  ADORA2B mediated anti-inflammatory cytokines production 0.2348337 0.6272866
## 4:                AKT phosphorylates targets in the cytosol 0.2689938 0.6292054
## 5:                                    ALK mutants bind TKIs 0.4537815 0.8379488
## 6: AMPK inhibits chREBP transcriptional activation activity 0.5134100 0.8782013
##       log2err         ES        NES size     leadingEdge
## 1: 0.05248276 -0.5631579 -0.7500433    1            3159
## 2: 0.09167952  0.8105263  1.0916488    1              23
## 3: 0.12384217  0.7301587  1.1690604    2       5576,5573
## 4: 0.11776579 -0.8842105 -1.1776380    1            7249
## 5: 0.08705159  0.5797872  1.0371355    3 27436,5573,4869
## 6: 0.07550153  0.7421053  0.9994966    1           51085
data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                     pathway      pval      padj
## 1:                                   2-LTR circle formation 0.9798387 0.9964546
## 2:                   ABC-family proteins mediated transport 0.7096774 0.9794140
## 3:  ADORA2B mediated anti-inflammatory cytokines production 0.4896074 0.8877471
## 4:                AKT phosphorylates targets in the cytosol 0.2986248 0.8463915
## 5:                                    ALK mutants bind TKIs 0.1310680 0.6564230
## 6: AMPK inhibits chREBP transcriptional activation activity 0.7762097 0.9794140
##       log2err         ES        NES size     leadingEdge
## 1: 0.04697587  0.5105263  0.6878743    1            3159
## 2: 0.06197627  0.6368421  0.8580700    1              23
## 3: 0.08809450  0.6680607  1.0398767    2       5573,5576
## 4: 0.10797236 -0.8526316 -1.1405130    1            7249
## 5: 0.19189224  0.7503226  1.3940814    3 5573,27436,4869
## 6: 0.05773085  0.6105263  0.8226125    1           51085
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                     pathway      pval      padj
## 1:                                   2-LTR circle formation 0.9831224 0.9924812
## 2:                   ABC-family proteins mediated transport 0.4943609 0.9094893
## 3:  ADORA2B mediated anti-inflammatory cytokines production 0.1808279 0.7197245
## 4:                AKT phosphorylates targets in the cytosol 0.2848101 0.7675439
## 5:                                    ALK mutants bind TKIs 0.3233831 0.7675439
## 6: AMPK inhibits chREBP transcriptional activation activity 0.6466165 0.9924812
##       log2err         ES        NES size     leadingEdge
## 1: 0.04889708 -0.5105263 -0.6860710    1            3159
## 2: 0.07647671  0.7631579  1.0291523    1              23
## 3: 0.15214492  0.8042328  1.2906577    2       5576,5573
## 4: 0.11573445 -0.8578947 -1.1528822    1            7249
## 5: 0.11828753  0.6337490  1.1660308    3 27436,5573,4869
## 6: 0.06307904  0.6789474  0.9155907    1           51085

EC vs E

data_diff_EC_vs_E_pST <- test_diff(pST_se_Set4, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                     pathway      pval      padj
## 1:                                   2-LTR circle formation 0.6859504 0.9931178
## 2:                   ABC-family proteins mediated transport 0.5801527 0.9931178
## 3:  ADORA2B mediated anti-inflammatory cytokines production 0.6389685 0.9931178
## 4:                AKT phosphorylates targets in the cytosol 0.6198347 0.9931178
## 5:                                    ALK mutants bind TKIs 0.3229167 0.9931178
## 6: AMPK inhibits chREBP transcriptional activation activity 0.6125954 0.9931178
##       log2err         ES        NES size     leadingEdge
## 1: 0.06479434  0.6421053  0.8669413    1            3159
## 2: 0.06911985 -0.7315789 -0.9688412    1              23
## 3: 0.08528847  0.5455885  0.8705113    2            5573
## 4: 0.06977925  0.6947368  0.9380021    1            7249
## 5: 0.14290115  0.5797872  1.1132263    3 5573,27436,4869
## 6: 0.06643641 -0.7157895 -0.9479310    1           51085
#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set4, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                     pathway      pval      padj
## 1:                                   2-LTR circle formation 0.7917526 0.9387918
## 2:                   ABC-family proteins mediated transport 0.2898273 0.9387918
## 3:  ADORA2B mediated anti-inflammatory cytokines production 0.7563353 0.9387918
## 4:                AKT phosphorylates targets in the cytosol 0.7298969 0.9387918
## 5:                                    ALK mutants bind TKIs 0.2940000 0.9387918
## 6: AMPK inhibits chREBP transcriptional activation activity 0.5297505 0.9387918
##       log2err         ES        NES size leadingEdge
## 1: 0.05785298 -0.6052632 -0.8087060    1        3159
## 2: 0.10839426  0.8684211  1.1542653    1          23
## 3: 0.05736674 -0.5438133 -0.8248791    2        5573
## 4: 0.06170541 -0.6368421 -0.8508994    1        7249
## 5: 0.11012226 -0.6967521 -1.1798549    3        5573
## 6: 0.07399014  0.7421053  0.9863721    1       51085

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

Session Info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.2             forcats_1.0.0              
##  [3] stringr_1.5.0               dplyr_1.1.2                
##  [5] purrr_1.0.1                 readr_2.1.4                
##  [7] tidyr_1.3.0                 tibble_3.2.1               
##  [9] ggplot2_3.4.2               tidyverse_2.0.0            
## [11] mdatools_0.14.0             SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] MatrixGenerics_1.10.0       matrixStats_1.0.0          
## [17] DEP_1.20.0                  org.Hs.eg.db_3.16.0        
## [19] AnnotationDbi_1.60.2        IRanges_2.32.0             
## [21] S4Vectors_0.36.2            Biobase_2.58.0             
## [23] BiocGenerics_0.44.0         fgsea_1.24.0               
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.4.15        fastmatch_1.1-3        plyr_1.8.8            
##   [4] igraph_1.5.0.1         gmm_1.8                lazyeval_0.2.2        
##   [7] shinydashboard_0.7.2   crosstalk_1.2.0        BiocParallel_1.32.6   
##  [10] digest_0.6.33          foreach_1.5.2          htmltools_0.5.5       
##  [13] fansi_1.0.4            magrittr_2.0.3         memoise_2.0.1         
##  [16] cluster_2.1.4          doParallel_1.0.17      tzdb_0.4.0            
##  [19] limma_3.54.2           ComplexHeatmap_2.14.0  Biostrings_2.66.0     
##  [22] imputeLCMD_2.1         sandwich_3.0-2         timechange_0.2.0      
##  [25] colorspace_2.1-0       blob_1.2.4             xfun_0.39             
##  [28] crayon_1.5.2           RCurl_1.98-1.12        jsonlite_1.8.7        
##  [31] impute_1.72.3          zoo_1.8-12             iterators_1.0.14      
##  [34] glue_1.6.2             hash_2.2.6.2           gtable_0.3.3          
##  [37] zlibbioc_1.44.0        XVector_0.38.0         GetoptLong_1.0.5      
##  [40] DelayedArray_0.24.0    shape_1.4.6            scales_1.2.1          
##  [43] pheatmap_1.0.12        vsn_3.66.0             mvtnorm_1.2-2         
##  [46] DBI_1.1.3              Rcpp_1.0.11            plotrix_3.8-2         
##  [49] mzR_2.32.0             viridisLite_0.4.2      xtable_1.8-4          
##  [52] clue_0.3-64            reactome.db_1.82.0     bit_4.0.5             
##  [55] preprocessCore_1.60.2  sqldf_0.4-11           MsCoreUtils_1.10.0    
##  [58] DT_0.28                htmlwidgets_1.6.2      httr_1.4.6            
##  [61] gplots_3.1.3           RColorBrewer_1.1-3     ellipsis_0.3.2        
##  [64] farver_2.1.1           pkgconfig_2.0.3        XML_3.99-0.14         
##  [67] sass_0.4.7             utf8_1.2.3             STRINGdb_2.10.1       
##  [70] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.1           
##  [73] later_1.3.1            munsell_0.5.0          tools_4.2.3           
##  [76] cachem_1.0.8           cli_3.6.1              gsubfn_0.7            
##  [79] generics_0.1.3         RSQLite_2.3.1          fdrtool_1.2.17        
##  [82] evaluate_0.21          fastmap_1.1.1          mzID_1.36.0           
##  [85] yaml_2.3.7             knitr_1.43             bit64_4.0.5           
##  [88] caTools_1.18.2         KEGGREST_1.38.0        ncdf4_1.21            
##  [91] mime_0.12              compiler_4.2.3         rstudioapi_0.15.0     
##  [94] plotly_4.10.2          png_0.1-8              affyio_1.68.0         
##  [97] stringi_1.7.12         bslib_0.5.0            highr_0.10            
## [100] MSnbase_2.24.2         lattice_0.21-8         ProtGenerics_1.30.0   
## [103] Matrix_1.6-0           tmvtnorm_1.5           vctrs_0.6.3           
## [106] pillar_1.9.0           norm_1.0-11.1          lifecycle_1.0.3       
## [109] BiocManager_1.30.21.1  jquerylib_0.1.4        MALDIquant_1.22.1     
## [112] GlobalOptions_0.1.2    data.table_1.14.8      cowplot_1.1.1         
## [115] bitops_1.0-7           httpuv_1.6.11          R6_2.5.1              
## [118] pcaMethods_1.90.0      affy_1.76.0            promises_1.2.0.1      
## [121] KernSmooth_2.23-22     codetools_0.2-19       MASS_7.3-60           
## [124] gtools_3.9.4           assertthat_0.2.1       chron_2.3-61          
## [127] proto_1.0.0            rjson_0.2.21           withr_2.5.0           
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3         hms_1.1.3             
## [133] grid_4.2.3             rmarkdown_2.23         shiny_1.7.4.1
knitr::knit_exit()